Explore how we helped visionary companies build scalable, high-performance digital products.
We embedded an intelligent fraud detection layer into an existing KYC platform — combining real-time face recognition, liveness detection, behavioral signals, and a multi-dimensional feature store to block fraudulent verifications before they reach compliance teams.
The client's KYC platform was processing thousands of verifications daily but lacked the intelligence to catch sophisticated fraud attempts — including printed photo spoofing, video replay attacks, deepfakes, and identity theft through stolen selfies. Manual review could not scale, and false negatives were reaching onboarding downstream.
We designed and integrated a Python-based AI microservice into the existing KYC flow via REST APIs and micro frontend techniques. A React component library rendered the liveness capture UI within the host app without requiring a rewrite of existing screens. The AI layer ran on a dedicated inference cluster and communicated asynchronously, keeping the KYC flow non-blocking and audit-ready.
The AI service was shipped as a Python FastAPI microservice — stateless, horizontally scalable, and containerized. The React liveness-capture module was embedded into the client's existing app as a micro frontend via Module Federation, requiring zero changes to the host app's router or auth layer. Face embeddings and fraud signals were stored in a vector database (Redis) enabling sub-10ms similarity lookups at scale. A structured audit log captured every decision with confidence scores for compliance reporting.
We embedded a production-grade agentic chatbot into an existing travel agency application — combining Retrieval-Augmented Generation, semantic routing, live inventory queries, and intelligent human handoff to deliver instant, accurate answers while keeping LLM costs under control at scale.
The client's support team was drowning in repetitive queries — seat availability, hotel room inventory, booking status, and generic FAQs — all being handled manually. Response times were high, after-hours coverage was non-existent, and LLM prototyping attempts had resulted in hallucinations and uncontrolled API costs. They needed an intelligent assistant that was accurate, cost-conscious, and could hand off gracefully to a human agent when needed.
We delivered a RAG-based agentic chatbot integrated directly into the client's existing React application via a lightweight SDK drop-in — no rebuild required. The agent is backed by a Python / LangGraph orchestration layer that handles intent detection, model routing, tool calls via MCP servers, and a Redis-powered infrastructure stack handling caching, vector search, guardrails, and semantic deduplication — all running on Kubernetes for elastic scaling under peak travel seasons.
The entire stack — orchestration layer, MCP servers, embedding pipeline, and guardrail service — runs on Kubernetes with horizontal pod autoscaling, handling traffic spikes during peak booking seasons without manual intervention. Redis serves as the single AI infrastructure backbone: vector store for RAG embeddings, semantic cache for LLM response reuse, guardrail rule store, and model routing decision cache. All agent steps are logged with full trace context for debugging, cost attribution, and compliance audit.
A comprehensive fintech ecosystem enabling end users to discover, compare, and apply for financial products — credit cards, personal loans, CIBIL score improvement, and more — all from a single, beautifully designed platform.
The client needed a consumer-facing financial services marketplace where users could explore and apply for various financial products. The platform had to be fast, mobile-friendly, and capable of handling sensitive financial data with compliance-grade security.
We started with a high-conversion React landing page, then layered on a scalable Node.js + Express backend with RESTful APIs. The product catalog displays offerings with live eligibility checks and streamlined application flows.
We managed the full DevOps lifecycle — containerized services with Docker, orchestrated on Kubernetes, and deployed via automated CI/CD pipelines with zero-downtime rolling updates and instant rollback capabilities.
A full-stack telehealth product suite — mobile app, web dashboard, and robust backend — enabling patients to consult doctors, manage health records, pay for consultations, and receive prescriptions, all within a single ecosystem. Think Practo, built from scratch.
The client envisioned a Practo-like ecosystem where patients and doctors could connect seamlessly. The product needed a mobile app for patients, a web portal for doctors, real-time video/audio calling, secure payments, and intelligent WhatsApp integration — all HIPAA-aware and scalable.
A Flutter mobile app for patients with onboarding, health history, appointment booking, in-app audio/video consultations, payment gateway, and prescription management. A React web dashboard for doctors to manage appointments and patient records. A robust Node.js + Python backend powering everything.
Fully managed DevOps — microservices containerized with Docker, orchestrated on Kubernetes, with automated CI/CD pipelines for the mobile app, web dashboard, and backend services. Automated testing, staging environments, and production deployments with zero-downtime rollouts.